基于数据分析的飞行实践考试不通过关联规则挖掘  

Data Analysis-based Association Rule Mining of Flight Practice Test Failure

作　　者：赵赶超 常向阳 马姗 闫东峰 Zhao Ganchao;Chang Xiangyang;Ma Shan;Yan Dongfeng(Civil Aviation Flight University of China,Guanghan 618307 Sichuan China)

机构地区：[1]中国民用航空飞行学院,四川广汉618307

出　　处：《中国民航飞行学院学报》2023年第6期31-35,共5页Journal of Civil Aviation Flight University of China

摘　　要：飞行实践考试是对飞行学员飞行训练效果的综合检验,是航空人才培养过程中的重要环节。为探究飞行学员飞行实践考试不通过的关键诱因,降低飞行实践考试不通过率,本文使用Apriori数据挖掘算法对飞行实践考试不通过的实测数据进行建模,从人、机、环、管四个方面分析飞行实践考试挂科的原因,旨在为提高飞行实践考试通过率提供参考。The flight practice test is a comprehensive test of the effect of flight training on student pilots.It is an important link in the process of cultivating aviation talents.To explore the key causes of flight practice test failure for student pilots and reduce the failure rate of flight practice tests,this thesis uses the Apriori data mining algorithm to model the measured data of failing in the flight practice test,analyzes the causes for failing in the flight practice tests from four aspects:the human,the aircraft,the environment and the management and intends to provide reference for improving the pass rate of flight practice tests.

关 键 词：APRIORI算法 数据挖掘 飞行实践考试 

分 类 号：V323[航空宇航科学与技术—人机与环境工程] TP311.13[自动化与计算机技术—计算机软件与理论]